VATEX
VaTeX: A Large-Scale, High-Quality Multilingual Dataset for Video-and-Language Research. Contains over 41,250 videos and 825,000 captions in both English and Chinese, with over 206,000 English-Chinese parallel translation pairs. Supports multilingual video captioning and video-guided machine translation tasks.
Nova Lite from Amazon currently leads the VATEX leaderboard with a score of 0.778 across 2 evaluated AI models.
What VATEX measures
VATEX is a multimodal benchmark that evaluates large language models on language, multimodal, video, and vision tasks. LLM Stats tracks 2 models on this benchmark, with a maximum possible score of 1. Current average across reported models is 0.8, with the leader reaching 0.8.
Compare leaders on the best AI for language, best AI for multimodal, best AI for video and best AI for vision leaderboards.
Publication
- Paper
- VATEX: A Large-Scale, High-Quality Multilingual Dataset for Video-and-Language Research
- Authors
- Xin Wang, Jiawei Wu, Junkun Chen, Lei Li, and 2 others
- Published
- arXiv
- 1904.03493
Abstract
We present a new large-scale multilingual video description dataset, VATEX, which contains over 41,250 videos and 825,000 captions in both English and Chinese. Among the captions, there are over 206,000 English-Chinese parallel translation pairs. Compared to the widely-used MSR-VTT dataset, VATEX is multilingual, larger, linguistically complex, and more diverse in terms of both video and natural language descriptions. We also introduce two tasks for video-and-language research based on VATEX: (1) Multilingual Video Captioning, aimed at describing a video in various languages with a compact unified captioning model, and (2) Video-guided Machine Translation, to translate a source language description into the target language using the video information as additional spatiotemporal context. Extensive experiments on the VATEX dataset show that, first, the unified multilingual model can not only produce both English and Chinese descriptions for a video more efficiently, but also offer improved performance over the monolingual models. Furthermore, we demonstrate that the spatiotemporal video context can be effectively utilized to align source and target languages and thus assist machine translation. In the end, we discuss the potentials of using VATEX for other video-and-language research.
Progress Over Time
Interactive timeline showing model performance evolution on VATEX
VATEX Leaderboard
FAQ
Common questions about VATEX.
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